High performance and grid computing with partitioning quality of service control

ABSTRACT

High performance computing (HPC) and grid computing processing for seismic and reservoir simulation are performed without impacting or losing processing time in case of failures. A Data Distribution Service (DDS) standard is implemented in High Performance Computing (HPC) and grid computing platforms, to avoid the shortcomings of current Message Passing Interface (MPI) communication between computing modules, and provide quality of service (QoS) for such applications. QoS properties of the processing can be controlled. A “partitioning” quality of service is provided and the computer can be logically segregated into several “logical partitions” so that a computer can have several publisher nodes, serving several groups of compute nodes, and running different applications independently.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to, each ofcommonly-owned U.S. patent application Ser. No. 14/490,894, filed Sep.19, 2014, titled “High Performance and Grid Computing With ReliabilityQuality of Service Control”; U.S. patent application Ser. No.14/490,862, filed Sep. 19, 2014, titled “High Performance and GridComputing With History Quality of Service Control”, filed Sep. 19, 2014;and U.S. patent application Ser. No. 13/649,286, filed Oct. 11, 2012,titled, “High Performance and Grid Computing With Quality of ServiceControl” which claims priority to U.S. Provisional Patent ApplicationNo. 61/545,766 filed Oct. 11, 2011 and has issued as U.S. Pat. No.8,874,804.

A related application, “HIGH PERFORMANCE AND GRID COMPUTING WITH FAULTTOLERANT DATA DISTRIBUTORS QUALITY OF SERVICE,” Ser. No. ______,(Attorney Docket No. 004159.005438) naming the same co-inventors isbeing filed of even date herewith.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to high performance and grid computing ofdata for exploration and production of hydrocarbons, such ascomputerized simulation of hydrocarbon reservoirs in the earth,geological modeling, and processing of seismic survey data, and inparticular to quality of service (QoS) control of such computing.

2. Description of the Related Art

In the oil and gas industries, massive amounts of data are required tobe processed for computerized simulation, modeling and analysis forexploration and production purposes. For example, the development ofunderground hydrocarbon reservoirs typically includes development andanalysis of computer simulation models of the reservoir. Theseunderground hydrocarbon reservoirs are typically complex rock formationswhich contain both a petroleum fluid mixture and water. The reservoirfluid content usually exists in two or more fluid phases. The petroleummixture in reservoir fluids is produced by wells drilled into andcompleted in these rock formations.

A geologically realistic model of the reservoir, and the presence of itsfluids, also helps in forecasting the optimal future oil and gasrecovery from hydrocarbon reservoirs. Oil and gas companies have come todepend on geological models as an important tool to enhance the abilityto exploit a petroleum reserve. Geological models of reservoirs andoil/gas fields have become increasingly large and complex.

In simulation and geological models, the reservoir is organized into anumber of individual cells. Seismic data with increasing accuracy haspermitted the cells to be on the order of 25 meters areal (x and y axis)intervals. For what are known as giant reservoirs, the number of cellsis the least hundreds of millions, and reservoirs of what is known asgiga-cell size (a billion cells or more) are encountered.

Similar considerations of data volume are also presented in seismic dataprocessing. Seismic data obtained from surveys over large areas of theearth's surface such as above giant reservoirs, has been acquired andmade available in increased volumes. In processing vast amounts of dataof all three of the types described above, processing time was animportant consideration.

Three types of computer systems have been available for processing thevast amounts of data of the types encountered in petroleum explorationand production. These are supercomputers, high performance computing(HPC) and grid computing. Typically, supercomputers are speciallydesigned for particular calculation intensive tasks. An HPC system takesthe form of a group of powerful workstations or servers, joined togetheras a network to function as one supercomputer. Grid computing involves amore loosely coupled, heterogeneous and often dispersed network ofworkstations or servers than HPC.

So far as is known, existing distributed memory HPC and grid computingsystems did not provide proper quality of service (QoS) basedcommunication because of two limitations. First, standard communicationlibraries such as Message Passing Interface (MPI) and Parallel VirtualMachine (PVM) did not provide a capability for applications to specifyservice quality for computation and communication. Second, modernhigh-speed interconnects such as Infiniband, Myrinet, Quadrics andGigabit Ethernet were optimized for performance rather than forpredictability of communication latency and bandwidth.

There has been, so far as is known, little attention given to QoScontrol in high performance and grid computing. HPC users have witnesseda dramatic increase in performance over the last ten years with regardto the HPC systems. What used to take one month of HPC computation timein the ten years ago, is now taking only a few hours to run in currentsystems.

In view of this, the simplest remedy to users for a data accuracyfailure rate during a routine computation run has been resubmitting theprocessing data run after disregarding or offlining (or what is known asfencing) the problematic node/core. However, the hours of the crashedjob were thus discarded and wasted. Moreover, in the case of a moreextensive data processing run which would require several days or evenweeks to perform, resubmitting the entire data set for processing wasrequired. This was duplicative and time consuming

U.S. Pat. No. 7,526,418, which is owned by the assignee of the presentapplication, relates to a simulator for giant hydrocarbon reservoirscomposed of a massive number of cells. The simulator mainly used highperformance computers (HPC). Communication between the cluster computerswas performed according to conventional, standard methods, such as MPImentioned above and Open MP.

U.S. Published Patent Application No. 2011/0138396 related to a datadistribution mechanism in HPC clusters. The focus of the systemdescribed was methodology to enable data to be distributed rapidly tovarious computation nodes in an HPC cluster. Thus, the focus andteachings of this system were improving processing speed by more rapidlydistributing data to the cluster nodes.

SUMMARY OF THE INVENTION

Briefly, the present invention provides a new and improved computerimplemented method of computerized processing in a data processingsystem of data for exploration and production of hydrocarbons. Theprocessing is performed in a data processing system which includes aplurality of master nodes established as publishers of segments ofexploration and production data to be separately processed by the masternodes, based on an established quality of service standard profileincluding a partition setting for the master nodes establishingpartitions of the data processing system for separate processing of thesegments of the exploration and production data. The data processingsystem for performing the processing also includes a plurality ofprocessor nodes designated as subscribers in the established partitionsto receive an assigned segment of the exploration and production datafor processing from a designated one of the plurality of the publishermaster nodes according to the partition setting of the quality ofservice standard profile, and a data memory. The computer processingaccording to the present invention transmits the established quality ofservice standard profile from the publisher master nodes to thesubscriber processor nodes. Partitions of the data processing system forseparate processing of the segments of the exploration and productiondata are established with the publisher master nodes by the publishermaster nodes and the processor nodes being designated as subscriberprocessor nodes according to the partition setting. The designatedsubscriber processor nodes in the established partitions are furtherestablished as data writers to transfer the processed exploration andproduction data to the designated publisher master nodes of theestablished partitions. Source data samples of the exploration andproduction data being processed in each of the established partitionsare sent from the publisher master node to the designated subscriberprocessor nodes of the partition, and the transmitted exploration andproduction data is processed in the designated subscriber processornodes of the established partitions. The processed exploration andproduction data of the designated subscriber processor nodes at thepublisher master nodes of the established partitions is monitored. Thepublisher master nodes determine whether the designated subscriberprocessor nodes of the established partitions comply with thetransmitted established quality of service standard profile from thepublisher master node. If so, the processed exploration and productiondata from the designated subscriber processor nodes of the establishedpartitions which comply with the transmitted established quality ofservice standard profile are received at the publisher master nodes. Ifnot, the publisher master nodes inhibit transfer to the publisher masternode of the processed exploration and production data from thedesignated subscriber processor nodes of the established partitionswhich do not comply with the transmitted established quality of servicestandard profile. The processed exploration and production data receivedat the publisher master nodes for the separately processed segments ofthe of the exploration and production data are assembled in the datamemory of the data processing system.

The present invention also provides a new and improved data processingsystem for computerized processing of data for exploration andproduction of hydrocarbons. The data processing system includescomprising a plurality of master nodes established as publishers ofsegments of exploration and production data to be separately processedby the master nodes, based on an established quality of service standardprofile including a partition setting for the master nodes establishingpartitions of data processing system for separate processing of thesegments of the exploration and production data. The data processingsystem also includes a plurality of processor nodes designated assubscribers in the established partitions to receive an assigned segmentof the exploration and production data for processing from a designatedone of the plurality of the publisher master nodes according to thepartition setting of the quality of service standard profile, and a datamemory. Each of the master nodes transmits the established quality ofservice standard profile from the publisher master node to the processornodes. Partitions of the data processing system are established with thepublisher master nodes for separate processing of the segments of theexploration and production data by the publisher master nodes and theprocessor nodes designated as subscriber processor nodes according tothe partition setting. The designated subscriber processor nodes in theestablished partitions are further established as data writers totransfer the processed exploration and production data to the designatedpublisher master nodes of the established partitions. Source datasamples of the exploration and production data being processed in eachof the established partitions are sent from the publisher master node tothe designated subscriber processor nodes of the partition. Thedesignated subscriber processor nodes receive the segments of theexploration and production data and the established quality of servicestandard profile from the publisher master node, and process thetransmitted exploration and production data in the designated subscriberprocessor nodes of the established partitions. The master nodes monitorthe processed exploration and production data of the designatedsubscriber processor nodes at the publisher master nodes of theestablished partitions, and determine whether the designated subscriberprocessor nodes of the established partitions comply with thetransmitted established quality of service standard profile from thepublisher master node. If so, the processed exploration and productiondata are received from the designated subscriber processor nodes of theestablished partitions Which comply with the transmitted establishedquality of service standard profile. If not, the publisher master nodesinhibit transfer of the processed exploration and production data fromthe designated subscriber processor nodes of the established partitionswhich do not comply with the transmitted established quality of servicestandard profile. The processed exploration and production data from thedesignated subscriber processor nodes for the separately processedsegments of the exploration and production data which comply with thetransmitted established quality of service standard profile areassembled in the data memory.

The present invention further provides a new and improved data storagedevice having stored in a computer readable medium computer operableinstructions for causing a data processing system to process data forexploration and production of hydrocarbons. The data processing systemincludes a plurality of master nodes established as publishers ofsegments of exploration and production data to be separately processedby the master nodes, based on an established quality of service standardprofile including a partition setting for the master nodes establishingpartitions of data processing system for separate processing of thesegments of the exploration and production data. The data processingsystem also includes a plurality of processor nodes designated assubscribers in an established partition to receive an assigned segmentof the exploration and production data for processing from a designatedone of the plurality of the publisher master nodes according to thepartition setting of the quality of service standard profile, and a datamemory. The instructions stored in the data storage device causing thedata processing system to transmit the established quality of servicestandard profile from the publisher master nodes to the subscriberprocessor nodes, and establish with the publisher master nodes thepartitions of the data processing system for separate processing of thesegments of the exploration and production data by the publisher masternodes and the processor nodes designated as subscriber processor nodesaccording to the partition setting. The instructions further establishthe designated subscriber processor nodes in the established partitionsas data writers to transfer the processed exploration and productiondata to the designated publisher master nodes of the establishedpartitions. The instructions also send source data samples of theexploration and production data being processed in each of theestablished partitions from the publisher master node to the designatedsubscriber processor nodes of the partition. The instructions furthercause the data processing system to process the transmitted explorationand production data in the designated subscriber processor nodes of theestablished partitions. The instructions also cause the data processingsystem to monitor the processed exploration and production data of thedesignated subscriber processor nodes at the publisher master nodes ofthe established partitions. The instructions also cause the dataprocessing system to determine in the publisher master nodes whether thedesignated subscriber processor nodes of the established partitionscomply with the transmitted established quality of service standardprofile from the publisher master node. If so, the instructions causethe data processing system to receive at the publisher master nodes theprocessed exploration and production data from the designated subscriberprocessor nodes of the established partitions which comply with thetransmitted established quality of service standard profile. If not, theinstructions cause the data processing system to inhibit at thepublisher master nodes transfer to the publisher master node of theprocessed exploration and production data from the designated subscriberprocessor nodes of the established partitions which do not comply withthe transmitted established quality of service standard profile. Theinstructions cause the data processing system to assemble in the datamemory of the data processing system the processed exploration andproduction data received at the publisher master nodes for theseparately processed segments of the of the exploration and productiondata.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a prior art data processingsystem for high performance computing.

FIG. 2 is a schematic block diagram of a data processing system for highperformance and grid computing with quality of service control accordingto the present invention.

FIG. 3 is a functional block diagram of the data processing system ofFIG. 2 configured for high performance processing with quality ofservice control according to the present invention.

FIG. 4 is a functional block diagram of a set of data processing stepsperformed in the data processing system of FIGS. 2 and 3 for highperformance processing with quality of service control according to thepresent invention.

FIG. 5 is a functional block diagram of a set of data processing stepsperformed in the data processing system of FIGS. 2 and 3 for highperformance processing with quality of service control according to thepresent invention.

FIG. 6 is a functional block diagram indicating the interactiveoperation of processors of the data processing system of FIGS. 2 and 3during performance of the data processing steps of FIGS. 4 and 5.

FIG. 7 is a plot of runtime for the data processing system shown inFIGS. 2 and 3 for high performance processing with quality of servicecontrol with different sizes of input data in comparison with prior artMPI communication protocols.

FIG. 8 is a plot of network time delay in engaging a new processor nodefor different file sizes in the data processing system of FIGS. 2 and 3.

A DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention relates to high performance and grid computing ofdata for exploration and production of hydrocarbons, such ascomputerized simulation of hydrocarbon reservoirs in the earth,geological modeling, processing of seismic survey data and other typesof data gathered and processed to aid in the exploration and productionof hydrocarbons. For the purposes of the present invention data of theforegoing types are referred to herein as exploration and productiondata. The present invention is particularly adapted for processingexploration and production data where vast amounts of such data arepresent, such as in or around what are known as giant reservoirs.

In the drawings, FIG. 1 represents an example prior art high performancecomputing network P. The high performance computing network P isconfigured for parallel computing using the message passing interface(MPI) with a master ode 10 transferring data through what are known asserial heartbeat connections over data links 12 of a management network14 to a number of processor nodes 16. The processor nodes 16 areconfigured to communicate with each other as indicated at 18 accordingto the message passing interface (MPI) standard communication libraryduring parallel computing and processing of data. As has been set forth,so far as is known, standard communication libraries such as MessagePassing Interface (MPI) and Parallel Virtual Machine (PVM) did notprovide a capability for applications to spec service quality forcomputation and communication.

With the present invention, as is shown schematically in FIG. 2 in adata processing system D one or more master nodes 20 of a CPU 22 and agroup of processor or worker nodes 24 operating as a network arrangedfor high performance or grid computing, depending on the configurationof the network, of exploration and production data. In order for thedata processing system D to be capable, if required, of processingseveral applications independently at the same time, there are typicallya plurality of master nodes 20, as shown in FIG. 2.

As will be set forth, the data processing system D processes explorationand production data with a controllable specified quality of service(QoS) for the processing applications. Data processing system D operatesaccording to the processing techniques which are shown schematically inFIGS. 4, 5 and 6. Thus, high performance computing (HPC) and gridcomputing processing of exploration and production data are performedwithout impacting or losing processing time in case of failures. A datadistribution service (DDS) standard is implemented in the highperformance computing (HPC) and grid computing platforms of the dataprocessing system D, to avoid shortcomings of message passing interface(MPI) communication between computing modules, and provide quality ofservice (QoS) for such applications.

With the present invention, a partitioning quality of service (QoS) isprovided as indicated schematically at 25 in FIG. 2. With thepartitioning quality of service, each different master node 20 operatesindependently of the others with a separate partition R (FIG. 3) inconjunction with processor or worker nodes 24 allocated according to thepartitioning QoS and processes separate applications in the dataprocessing system D at the same time.

Considering now the data processing system according to the presentinvention, as illustrated in FIG. 2, the data processing system D isprovided as a processing platform for high performance computing (HPC)and grid computing of exploration and processing data. The dataprocessing system D includes one or more central processing units orCPU's 22. The CPU or CPU's 22 have associated therewith a reservoirmemory or database 26 for general input parameters, of a type and natureaccording to the exploration and production data being processed,whether reservoir simulation, geological modeling, seismic data or thelike.

A user interface 28 operably connected with the CPU 22 includes agraphical display 30 for displaying graphical images, a printer or othersuitable image forming mechanism and a user input device 32 to provide auser access to manipulate, access and provide output forms of processingresults, database records and other information.

The reservoir memory or database 26 is typically in a memory 34 of anexternal data storage server or computer 38. The reservoir database 26contains data including the structure, location and organization of thecells in the reservoir model, data general input parameters, as well asthe exploration and production data to be processed, as will bedescribed below.

The CPU or computer 22 of data processing system D includes the masternodes 20 and an internal memory 40 coupled to the master nodes 20 tostore operating instructions, control information and to serve asstorage or transfer buffers as required. The data processing system Dincludes program code 42 stored in memory 40. The program code 42,according to the present invention, is in the form of computer operableinstructions causing the master nodes 20 and processor nodes 24 totransfer the exploration and production data and control instructionsback and forth according to data distribution service (DDS)intercommunication techniques, as will be set forth.

It should be noted that program code 42 may be in the form of microcode,programs, routines, or symbolic computer operable languages that providea specific set of ordered operations that control the functioning of thedata processing system D and direct its operation. The instructions ofprogram code 42 may be stored in memory 40 or on computer diskette,magnetic tape, conventional hard disk drive, electronic read-onlymemory, optical storage device, or other appropriate data storage devicehaving a computer usable medium stored thereon. Program code 42 may alsobe contained on a data storage device as a computer readable medium.

The processor nodes 24 are general purpose, programmable data processingunits programmed to perform the processing of exploration and productiondata according to the present invention. The processor nodes 24 operateunder control of the master node 20, and the processing results obtainedare then assembled in memory 34 where the data are provided forformation with user interface 28 of output displays to form data recordsfor analysis and interpretation.

Although the present invention is independent of the specific computerhardware used, an example embodiment of the present invention ispreferably based on master nodes 20 and processor nodes 24 of an HPLinux cluster computer. It should be understood, however, that othercomputer hardware may also be used.

According to the present invention, the data processing system D whosecomponents are shown in FIG. 2 is configured as shown in FIG. 3according to the data distribution service (DDS) techniques. Asindicated in FIG. 3, within a partition R established by thepartitioning quality of service, the master node 20 of the pluralityshown in FIG. 2 is established as a publisher as indicated at 50. Thatmaster node 20 is the node responsible for dissemination anddistribution of the exploration and production data to be processed bythe processor nodes 24 of the partition R. The master node 20 includes apersistence service capability as shown at 51 to preserve data samplesso that they can be furnished to processor nodes Which replace failedprocessor nodes, as will be described. The master node 20 is also asindicated at 52 in FIG. 3 established as a data writer for theexploration and production data distributed for processing, so thatvalues of the data which is distributed can be established.

The processor nodes 24 of the established partition R operate accordingto data distribution service (DDS) and are each individually establishedas subscribers, as indicated at 54 in FIG. 3. Thus the processor nodesas subscribers are configured to operate as the processors responsibleassigned to receive the exploration and production data received as aresult of the subscriber relationship to the publisher 50 of the masternode 20. The processor nodes 24 are also configured as data readers asindicated at 56 in FIG. 3. As data readers 56, the processor nodes 24receive an allocated portion of the exploration and production data fromthe data writer 52 of the master node 20.

The DDS techniques of the present invention are further explained in thedata distribution service (DDS) standard. The DDS standard provides ascalable, platform-independent, and location-independent middlewareinfrastructure to connect information producers to consumers (i.e. nodesto nodes). DDS also supports many quality-of-service (QoS) policies,such as asynchronous, loosely-coupled, time-sensitive and reliable datadistribution at multiple layers (e.g., middleware, operating system, andnetwork).

The DDS methodology in an established partition R implements apublish/subscribe (PS) model for sending and receiving data, events, andcommands among the participant master node 20 and processor nodes 24 inthe processing of exploration and production data according to thepresent invention. As will be set forth, the master node serves as aprimary publisher and the processor nodes 24 function as the primarysubscribers. The processor nodes 24 are also configured to transfer theprocessed exploration and production data results to the master node 20,and the master node 20 is configured for this purpose as a subscriberfunction. Thus each node of the data processing system D can serve as apublisher, subscriber, or both simultaneously.

Nodes of the data processing system D that are producing information(publishers) create “topics” which are parameters of interest for thedata being processed, depending on the type of exploration andproduction data being processed. The nodes operating in the DDS modetake care of delivering the data samples to those subscribers thatindicate an interest in that topic. In a preferred computer networkaccording to the present invention, example implementations of DDSprovide low latency messaging (as fast as 65 microseconds between nodes)and high throughput (up to 950 Mbps).

As has been set forth, the present invention processes exploration andproduction data for giant reservoirs where vast amounts of data need tobe processed. An example type of processing performed is two-way datastreaming between master and processor nodes. Examples of data streamingusage in exploration and production data for giant reservoirs are U.S.Pat. Nos. 7,596,480; 7,620,534; 7,660,711; 7,526,418; and 7,809,537. Itshould be understood that the present invention may also be used inconnection with communication between nodes for HPC or grid computing ofexploration and production data for other types of processing inaddition to data streaming. The data communication according to thepresent invention between nodes for HPC and grid computing may also beused for other types of data, such as bioinformatics processing andcomputational fluid dynamics. The present invention is adapted for usein processing of large amounts of data which consume considerable timeand thus has an increased likelihood of system failure during the courseof processing.

FIG. 4 is a functional block diagram of a set 60 of data processingsteps performed by the master node 20 in the data processing system Daccording to the present invention. As shown at step 62 of FIG. 4, themaster node 20 is designated as the master publisher. Master node 20then spawns two threads using OpenMP to parallelize two functionalities.In the first thread, the master node 20 initializes during step 64 to bea publisher (P0) with selected QoS profile, which is predefined in anXML file. For example, in the data streaming embodiment described below,three main QoS policies are adopted. These are: durability, reliability,and history.

The “durability” of the QoS profile saves the published data topics sothat the data topics can be delivered to subscribing nodes that join thesystem at a later time, even if the publishing node has alreadyterminated. The persistence service can use a file system or arelational data base to save the status of the system.

The second QoS profile or policy, which is reliability, indicates thelevel of reliability requested by a DataReader or offered by aDataWriter. Publisher nodes may offer levels of reliability,parameterized by the number of past issues they can store for thepurpose of retrying transmissions. Subscriber nodes may then requestdifferent levels of reliable delivery, ranging from fast-but-unreliable“best effort” to highly reliable in-order delivery. Thus providing perdatastream reliability control. In case the reliability type is set to“RELIABLE”, the write operation on the DataWriter may be blocked if themodification would cause data to be lost or else cause one of theresource limited to be exceeded.

The third policy, history, controls the behavior of the communicationwhen the value of a topic changes before it is finally communicated tosome of its existing DataReader entities. If the type is set to“KEEP_LAST”, then the service will only attempt to keep the latestvalues of the topic and discard the older ones. In this case, aspecified value of depth of data retention regulates the maximum numberof values the service will maintain and deliver. The default (and mostcommon setting) for depth is one, indicating that only the most recentvalue should be delivered.

If the history type is set to “KEEP_ALL”, then the service will attemptto maintain and deliver all the values of the sent data to existingsubscribers. The resources that the service can use to keep this historyare limited by the settings of the RESOURCE_LIMITS QoS. If the limit isreached, then the behavior of the service will depend on the RELIABILITYQoS. If the reliability kind is “BEST_EFFORT”, then the old values willbe discarded. If the reliability setting is “RELIABLE”, then the servicewill block the DataWriter until it can deliver the necessary old valuesto all subscribers.

In the data streaming embodiment described herein, “DURABILITY” was usedand specified, because it was desirable for compute nodes to continuejoining the system whenever there is a failure in another node, and thusavoid the consequences of a node failure during an extended processingrun the third policy of history, “KEEP_LAST” was selected since thestreamed seismic or simulation data are not expected to change.Specifically, the seismic shots and simulation cell values are fixed andnot dynamic. In reliability QoS policy, RELIABLE, was specified as allthe values need to reach the subscribers and have complete answers ofthe data streaming. The requirement of data accuracy prohibited missingor losing elements while transmitting.

The present invention as described also provides a “partitioning”quality of service. The data processing system D can accordingly belogically segregated into several “logical partitions” R (FIG. 3) asshown schematically at 25 in FIG. 2. The data processing system D canthus have several publisher nodes 20, serving several groups of computeor processor nodes 24, and running different processing applicationsindependently of each other. The partitioning takes place as part ofpartition quality of services (QoS) settings. The partition quality ofservices (QoS) according to the present invention allows the dataprocessing system D to run more than one smaller reservoir simulation orseismic applications, based on the partitioning QoS. For example, a dataprocessing cluster (domain) with one hundred nodes can be partitioned astwo nodes designated as master nodes 50 in separate partitions R and theremaining ninety eight nodes as processor nodes 24. The first masterserver 50 serves forty eight processor nodes 24 as they are partitionedor identified by a specific partition identity or identifier code aspart of the sent quality of service and the second master server in asecond partition R serves fifty processor nodes 24 as they are alsoidentified by the sent quality of service (partition value) identifiercode.

As indicated at step 66, the thread also specifies the domain where allthe publishers and subscribers would work on, which is domain-0 in thepresent embodiment. Next, as indicated at step 68, a topic with anidentifying name is specified and a DataWriter (DW-0) is initializedduring step 70 under P0 using that topic. After that, the master node 20as publisher begins reading the data matrices from input to beprocessed, and initializes the data structure for the source sample (SS)by defining the matrices dimension and the number of processor nodes 24which are designated as processor or worker nodes for the processing ofthe exploration and production data. The master node then during step 72starts sending the Source sample SS-0 to the designated worker processornodes 24 through the DataWriter DW-0.

The second thread of master node 20 reverses the function of thread 0 bycreating an instance of a subscriber S0 as indicated at step 74 with dieselected QoS profile in Domain-0, in preparation to receive the partialresults from the worker nodes 24 (designated worker nodes 24 act assubscribers at the beginning and then as publishers at the end of theirprocessing). The master node 20 then listens to the worker nodes 24 toreceive processing results as indicated at 76 through the receivingsample RS-0 and verifies compliance with the selected QoS profile asindicated at step 78. The master node 20 checks the QoS profile while itis receiving data from the worker nodes. It only receives data fromthose working nodes 24 which are matching in their QoS. It will notreceive and will not negotiate with other worker nodes which haveincompatible QoS settings. The master node 20 then during step 80 storesthe verified processing results in data memory, and the stored resultsare available for output and display.

FIG. 5 is a functional block diagram of a set 84 of data processingsteps performed by the processor nodes 24 in the data processing systemD according to the present invention. On the subscribers' side (i.e.,the workers), each node 24 during step 86 initiates itself as asubscriber to the main publisher P0, assigns an ID to itself (Wi), andduring step 88 starts receiving the data for computational processing.The distribution of which data goes to which node 24 is done dynamicallyin a way that is determined by first identifying the data range taken byeach node according to the format of the data. As indicated at step 90,the QoS checking is done during the nodes' receiving process. The datais then processed during step 92 according to the processing required.Each worker node 24 during step 94 then sends its output with verifiedQoS through its DataWriter (DW-i) to the master node 20 for resultcollection. As indicated at step 96, QoS checking is done during thesending of data in step 94. Thus, when there is communication betweenthe publisher and subscriber, QoS checking is done to establish thisconnection.

FIG. 6 is a diagram 100 illustrating the interaction of the master node20 and worker processor nodes 24 in an example implementation of a datastreaming processing of exploration and production data. Theimplementation starts at step 102 by designating the master node 20 ofthe cluster as the main publisher. The master node 20, in turn, spawnstwo threads using OpenMP to parallelize its three main functions:initializing the node to be a publisher (P0) with the selected QoSprofile; specifying during step 104 the domain which the publishers andsubscribers are to work on, which is domain-0 in the exampleimplementation, and as shown at step 105 passing or transmitting thepartition number to each of the processor nodes 24. The partition numberis needed in the case of partitioning the entire computer to runmultiple independent jobs, that is, multiple master nodes 50 servingmultiple computes in the data processing system D concurrently. With the“partitioning” QoS, the data processing system D can thus be logicallysegregated into several “logical partitions” or segments so that acomputer can have several publisher nodes, as shown at 25 in FIG. 2, asserving several groups of compute nodes, and running differentapplications independently.

Specifying the domain is necessary in order to allow multiple groups ofpublishers and subscribers to work independently within a job,segmenting the job into several smaller sub-segments, if needed.Different algorithms may require different topics (or data sets) to besent independently by the same publisher, and each of these topics mayhave several DataWriters for redundancy.

Next, during step 106, a topic with the name “SEND_DATA” is created anda DataWriter (DW-0) is initialized during step 108 under P0 using thecreated topic. The reason for this hierarchy is that different topics(i.e. datasets) may be required to be sent independently by the samepublisher, and each of these topics may have several DataWriters forredundancy. After that, the publisher during step 110 starts reading theseismic shots or simulation cells data from input, and initializes thedata structure for the source sample (SS) by defining the matrices datasize and the number of workers. The publisher then during step 110starts sending the source sample SS-0 through the DataWriter DW-0. Theprocedure continues until step 112 indicates all sending has beencompleted. Processing then is continued at step 114.

As indicated at step 114, the second thread from master node 20 reversesthe function of thread 0 by creating an instance of a subscriber S0 withselected QoS profile in Domain-0, in preparation to receive the partialresults from the worker or processor nodes 24 that are working in thesame partition. Specifically, a DataReader (DR-0) is configured duringstep 116 at master node 20 for the subscriber S0 (the workers) that usestopic “RECV_RESULT”. Master node 20 then as indicated at step 118listens to the workers through the receiving sample RS-0 and outputs thepartial results. This processing continues as indicated at step 120until receiving of sample RS-0 is completed.

On the subscribers' side (i.e., the workers), during step 122 eachworker node 24 as discussed above and as shown in FIG. 5 initiatesitself as a subscriber to the main publisher P0, assigns an ID to itself(Wi), and starts receiving the seismic or simulation data for processingand subsequent result collection. The distribution of which data goes towhich node is done dynamically in a way that is determined by firstidentifying the data size taken by each node according to the format ofthe data.

In case of a node failure of a processor node 24 on the workers' side,the system administrator of master node 20 may initiate a new processornode 24 with the same ID of the failed worker. The new worker would readthe written checkpointed status as defined in the policy, re-read thesample from the persistence service 51, and resume the operation of thesystem.

It is important to mention that as a requirement for the durability QoS,all sent topics require DataWriters to match the configuration of thepersistent QoS policy configuration with the DataReaders. As aconsequence, a DataWriter that has an incompatible QoS with respect towhat the topic specified will not send its data to the persistentservice, and thus its status will not be saved. Similarly, a DataReaderthat has an incompatible QoS with respect to the specified in the topicwill not get data from it.

Thus FIG. 6 illustrates an example parallel processing often encounteredwith exploration and production data according to the present invention.The results were as expected: QoS could be controlled withfault-tolerance enabled when using the DDS techniques as adoptedmiddleware in such computer processing of exploration and productiondata. Specifically, compute nodes on the cluster were turned off andback on again, and the jobs continued to run with no need for a restart.

The present invention provides the ability to control QoS properties onHPC and Grids that affect predictability, overhead, resourceutilization, and aligns the scarce resources to the most criticalrequirements to these jobs.

To evaluate the performance of the present invention over conventionalHPC and compare it with processing using MPI, data streaming wasperformed on the same data set using both paradigms (i.e., DDS with thepresent invention and MPI) and evaluated them on the data processingsystem clusters of FIGS. 1 and 2. The data streaming processingalgorithm is computationally intensive with iterations, and it waschosen since it is a fundamental operation in many numerical linearalgebra applications used in processing of exploration and productiondata. An efficient implementation on parallel computers is an issue ofprime importance when providing such systems for processing ofexploration and production data.

FIG. 7 shows benchmarks to test the scalability and runtime of MPI andDDS by streamlining Reservoir Simulation data. It can be seen that theMPI version outperformed in terms of speed by taking around 6.76 secondsto stream 10 GB size of file, compared with 7.9 seconds using DDS. Thisis expected since the QoS is adding more computations and checks to thecommunication level. As has been mentioned, however, MPI and PVM arefocused on processing speed, with no effort to monitor accuracy orpossible processing deficiencies.

FIG. 8 shows the delay in engaging a new node according to the presentinvention, replacing a crashed node, while using the persistent serviceand durability, reliability and history QoS. This test is not applicableto a prior art MPI implementation. Since MPI implementations do notprovide a capability of specifying service quality for computation orcommunication. As indicated in FIG. 8, the delay in engaging a new nodeis proportional to the size of the matrices, since the persistentservice needs to resend all the previously published instances to thisnew node. During the benchmark testing, test results depicted in FIG. 8indicate that it took 15.2 seconds in a 10 GB data stream between nodes,while it took 72.2 seconds in a 50 GB test. This is to be compared withthe time required when it was necessary to resubmit the entire data setfor processing for data in large quantities.

The present invention thus adds several benefits of having quality ofservice in performance computing that are not available in thetraditional method (i.e. by using MPI as a middleware). Among thebenefits are that periodic publishers can indicate the speed at whichthey can publish by offering guaranteed update deadlines. By setting adeadline, a compliant publisher promises to send a new update at aminimum rate. Subscribers may then request data at that or any slowerrate.

Another benefit is that the continuing participation or activity ofentities can be monitored. The selected QoS offered with the presentinvention determines Whether an entity or a node is “active” (i.e.,alive). The application can also be informed via a listener when anentity is no longer responsive. A further benefit is that the presentinvention also permits the data processing System D to automaticallyarbitrate between multiple publishers of the same topic with a parametercalled “strength.” Subscribers receive from the strongest activepublisher. This provides automatic failover; if a strong publisherfails, all subscribers immediately receive updates from the backup(weaker) publisher.

It is also to be noted that QoS parameters exist with the DDS employmentto control the resources of the entire system, suggest latency budgets,set delivery order, attach user data prioritize messages, set resourceutilization limits and partition the system into namespaces. The presentinvention thus provides the ability to control QoS properties on HPC andgrids that affect predictability, overhead, resource utilization, andalign the computational resources to the most critical requirements.

The present invention is a feasible option for those applications inwhich QoS is considered a priority, or for those HPC batch jobs thatwould run for several days on commodity hardware, where the probabilityof failure is not negligible. Accordingly, the present inventionprovides the ability to control QoS properties on HPC and grids thataffect predictability, overhead, resource utilization, and aligns thescarce resources to the most critical requirements.

The invention has been sufficiently described so that a person withaverage knowledge in the matter may reproduce and obtain the resultsmentioned in the invention herein Nonetheless, any skilled person in thefield of technique, subject of the invention herein, may carry outmodifications not described in the request herein, to apply thesemodifications to a determined structure, or in the manufacturing processof the same, requires the claimed matter in the following claims; suchstructures shall be covered within the scope of the invention.

It should be noted and understood that there can be improvements andmodifications made of the present invention described in detail abovewithout departing from the spirit or scope of the invention as set forthin the accompanying claims.

What is claimed is:
 1. A computer implemented method of computerizedprocessing in a data processing system of data for exploration andproduction of hydrocarbons, the data processing system including aplurality of master nodes established as publishers of segments ofexploration and production data to be separately processed by the masternodes, based on an established quality of service standard profileincluding a partition setting for the master nodes establishingpartitions of the data processing system for separate processing of thesegments of the exploration and production data, a plurality ofprocessor nodes designated as subscribers in the established partitionsto receive an assigned segment of the exploration and production datafor processing from a designated one of the plurality of the publishermaster nodes according to the partition setting of the quality ofservice standard profile, and a data memory, the method comprising thecomputer processing steps of: (a) transmitting the established qualityof service standard profile from the publisher master nodes to thesubscriber processor nodes; (b) establishing with the publisher masternodes the partitions of the data processing system for separateprocessing of the segments of the exploration and production data by thepublisher master nodes and the processor nodes designated as subscriberprocessor nodes according to the partition setting; (c) furtherestablishing the designated subscriber processor nodes in theestablished partitions as data writers to transfer the processedexploration and production data to the designated publisher master nodesof the established partitions; (d) sending source data samples of theexploration and production data being processed in each of theestablished partitions from the publisher master node to the designatedsubscriber processor nodes of the partition; (e) processing thetransmitted exploration and production data in the designated subscriberprocessor nodes of the established partitions; (f) monitoring theprocessed exploration and production data of the designated subscriberprocessor nodes at the publisher master nodes of the establishedpartitions; (g) determining in the publisher master nodes whether thedesignated subscriber processor nodes of the established partitionscomply with the transmitted established quality of service standardprofile from the publisher master node; and (h) if so, receiving at thepublisher master nodes the processed exploration and production datafrom the designated subscriber processor nodes of the establishedpartitions which comply with the transmitted established quality ofservice standard profile; and (i) if not, inhibiting at the publishermaster nodes transfer to the publisher master node of the processedexploration and production data from the designated subscriber processornodes of the established partitions which do not comply with thetransmitted established quality of service standard profile; and (j)assembling in the data memory of the data processing system theprocessed exploration and production data received at the publishermaster nodes for the separately processed segments of the of theexploration and production data.
 2. The computer implemented method ofclaim 1, wherein the data processing system further includes a datadisplay, and further including the computer processing step of: formingan output display of the assembled processed exploration and productiondata.
 3. The computer implemented method of claim 1, wherein theexploration and production data comprises a reservoir simulation model.4. The computer implemented method of claim 1, wherein the explorationand production data comprises a geological model of a reservoir.
 5. Thecomputer implemented method of claim 1, wherein the exploration andproduction data comprises a seismic survey of the earth in a reservoir.6. The computer implemented method of claim 1, wherein the establishedpublisher master nodes of the partitions are further established as datawriters for sending a plurality of sets of the established partitions ofexploration and production data for processing by the designatedsubscriber processor nodes according to the established quality ofservice standard profile.
 7. The computer implemented method of claim 6,wherein the designated subscriber processor nodes in each of theestablished partitions are further established as data readers forreceiving and processing the a plurality of sets of the exploration andproduction data sent by the established publisher master node of thepartition.
 8. The computer implemented method of claim 1, wherein thepublisher master nodes of the partitions are further established as datareaders for receiving and transferring to the data memory the processedexploration and production data received from the data writers of thedesignated subscriber processor nodes of the partitions.
 9. The computerimplemented method of claim 1, wherein the partition setting of theestablished quality of service standard profile comprises an indicationof partition identity for the designated subscriber processor nodes asdata writers for transfer of exploration and production data.
 10. Thecomputer implemented method of claim 1, wherein the established qualityof service standard profile comprises a durability quality of serviceprofile.
 11. The computer implemented method of claim 1, wherein theestablished quality of service standard profile comprises a reliabilityquality of service profile.
 12. The computer implemented method of claim1, wherein the established quality of service standard profile comprisesa history quality of service profile.
 13. A data processing system forcomputerized processing of data for exploration and production ofhydrocarbons, the data processing system comprising a plurality ofmaster nodes established as publishers of segments of exploration andproduction data to be separately processed by the master nodes, based onan established quality of service standard profile including a partitionsetting for the master nodes establishing partitions of data processingsystem for separate processing of the segments of the exploration andproduction data, a plurality of processor nodes designated assubscribers in the established partitions to receive an assigned segmentof the exploration and production data for processing from a designatedone of the plurality of the publisher master nodes according to thepartition setting of the quality of service standard profile, and a datamemory, the data processing further comprising: (a) each of the masternodes performing the steps of: (1) transmitting the established qualityof service standard profile from the publisher master node to theprocessor nodes; (2) establishing with the publisher master nodes thepartitions of the data processing system for separate processing of thesegments of the exploration and production data by the publisher masternodes and the processor nodes designated as subscriber processor nodesaccording to the partition setting; (3) further establishing thedesignated subscriber processor nodes in the established partitions asdata writers to transfer the processed exploration and production datato the designated publisher master nodes of the established partitions;(4) sending source data samples of the exploration and production databeing processed in each of the established partitions from the publishermaster node to the designated subscriber processor nodes of thepartition; (b) the plurality of processor nodes performing the steps of:(1) receiving in the designated subscriber processor nodes the segmentsof the exploration and production data and the established quality ofservice standard profile from the publisher master node; (2) processingthe transmitted exploration and production data in the designatedsubscriber processor nodes of the established partitions; and (c) themaster nodes further performing the steps of: (1) monitoring theprocessed exploration and production data of the designated subscriberprocessor nodes at the publisher master nodes of the establishedpartitions; (2) determining in the publisher master nodes whether thedesignated subscriber processor nodes of the established partitionscomply with the transmitted established quality of service standardprofile from the publisher master node; (3) if so, receiving theprocessed exploration and production data from the designated subscriberprocessor nodes of the established partitions which comply with thetransmitted established quality of service standard profile; and (4) ifnot, inhibiting at the publisher master nodes transfer of the processedexploration and production data from the designated subscriber processornodes of the established partitions which do not comply with thetransmitted established quality of service standard profile; and (5)assembling in the data memory the processed exploration and productiondata from the designated subscriber processor nodes for the separatelyprocessed segments of the exploration and production data which complywith the transmitted established quality of service standard profile.14. The data processing system of claim 13, further including a datadisplay, and wherein the master node further performs the step of:forming an output display of the assembled processed exploration andproduction data.
 15. The data processing system of claim 13, wherein theexploration and production data comprises a reservoir simulation model.16. The data processing system of claim 13, wherein the exploration andproduction data comprises a geological model of a reservoir.
 17. Thedata processing system of claim 13, wherein the exploration andproduction data comprises a seismic survey of the earth in a reservoir.18. The data processing system of claim 13, wherein the establishedpublisher master nodes of the partitions are further established as datawriters for sending a plurality of sets of the established partitions ofexploration and production data for processing by the designatedsubscriber processor nodes according to the established quality ofservice standard profile.
 19. The data processing system of claim 18,wherein the designated subscriber processor nodes in each of theestablished partitions are further established as data readers forreceiving and processing the a plurality of sets of the exploration andproduction data sent by the established publisher master node of thepartition.
 20. The data processing system of claim 13, wherein thepublisher master nodes of the partitions are further established as datareaders for receiving and transferring to the data memory the processedexploration and production data received from the data writers of thedesignated subscriber processor nodes of the partition.
 21. The dataprocessing system of claim 13, wherein the wherein the partition settingof the established quality of service standard profile comprises anindication of partition identity for the designated subscriber processornodes as data writers for transfer of exploration and production data.22. The data processing system of claim 13, established quality ofservice standard profile comprises a durability quality of serviceprofile.
 23. The data processing system of claim 13, established qualityof service standard profile comprises a reliability quality of serviceprofile.
 24. The data processing system of claim 13, established qualityof service standard profile comprises a history quality of serviceprofile.
 25. A data storage device having stored in a non-transitorycomputer readable storage medium computer operable instructions forcausing a data processing system to process data for exploration andproduction of hydrocarbons, the data processing system including aplurality of master nodes established as publishers of segments ofexploration and production data to be separately processed by the masternodes, based on an established quality of service standard profileincluding a partition setting for the master nodes establishingpartitions of data processing system for separate processing of thesegments of the exploration and production data, a plurality ofprocessor nodes designated as subscribers in an established partition toreceive an assigned segment of the exploration and production data forprocessing from a designated one of the plurality of the publishermaster nodes according to the partition setting of the quality ofservice standard profile, and a data memory, the instructions stored inthe data storage device causing the data processing system to performthe steps of: (a) transmitting the established quality of servicestandard profile from the publisher master nodes to the subscriberprocessor nodes; (b) establishing with the publisher master nodes thepartitions of the data processing system for separate processing of thesegments of the exploration and production data by the publisher masternodes and the processor nodes designated as subscriber processor nodesaccording to the partition setting; (c) further establishing thedesignated subscriber processor nodes in the established partitions asdata writers to transfer the processed exploration and production datato the designated publisher master nodes of the established partitions;(d) sending source data samples of the exploration and production databeing processed in each of the established partitions from the publishermaster node to the designated subscriber processor nodes of thepartition; (e) processing the transmitted exploration and productiondata in the designated subscriber processor nodes of the establishedpartitions; (f) monitoring the processed exploration and production dataof the designated subscriber processor nodes at the publisher masternodes of the established partitions; (g) determining in the publishermaster nodes whether the designated subscriber processor nodes of theestablished partitions comply with the transmitted established qualityof service standard profile from the publisher master node; and (h) ifso, receiving at the publisher master nodes the processed explorationand production data from the designated subscriber processor nodes ofthe established partitions which comply with the transmitted establishedquality of service standard profile; and (i) if not, inhibiting at thepublisher master nodes transfer to the publisher master node of theprocessed exploration and production data from the designated subscriberprocessor nodes of the established partitions which do not comply withthe transmitted established quality of service standard profile; and (j)assembling in the data memory of the data processing system theprocessed exploration and production data received at the publishermaster nodes for the separately processed segments of the of theexploration and production data.
 26. The data storage device of claim25, wherein the data processing system further includes a data display,and wherein the instructions further cause the data processing system toperform the step of: forming an output display of the assembledprocessed exploration and production data.
 27. The data storage deviceof claim 25, wherein the exploration and production data comprises areservoir simulation model.
 28. The data storage device of claim 25,wherein the exploration and production data comprises a geological modelof a reservoir.
 29. The data storage device of claim 25, wherein theexploration and production data comprises a seismic survey of the earthin a reservoir.
 30. The data storage device of claim 25, wherein theestablished publisher master nodes of the partitions are furtherestablished as data writers for sending a plurality of sets of theestablished partitions of exploration and production data for processingby the designated subscriber processor nodes according to theestablished quality of service standard profile.
 31. The data storagedevice of claim 30, wherein the designated subscriber processor nodes ineach of the established partitions are further established as datareaders for receiving and processing the a plurality of sets of theexploration and production data sent by the established publisher masternode of the partition.
 32. The data storage device of claim 25, whereinthe publisher master nodes of the partitions are further established asdata readers for receiving and transferring to the data memory theprocessed exploration and production data received from the data writersof the designated subscriber processor nodes of the partitions.
 33. Thedata storage device of claim 25, wherein the partition setting of theestablished quality of service standard profile comprises an indicationof partition identity for the designated subscriber processor nodes asdata writers for transfer of exploration and production data.
 34. Thedata storage device of claim 25, wherein the established quality ofservice standard profile comprises a durability quality of serviceprofile.
 35. The data storage device of claim 25, wherein theestablished quality of service standard profile comprises a reliabilityquality of service profile.
 36. The data storage device of claim 25,wherein the established quality of service standard profile comprises ahistory quality of service profile.